arXiv:2607. 16819v1 Announce Type: new Abstract: In recent years, large-scale vision-language models have been driving a paradigm shift in intelligent remote sensing image interpretation.
By Yi Yang, Xiaokun Zhang, Yuxuan Li, Ruyi Zhang, Xinpeng Zhou, Haipeng Wang
arXiv:2605. 09366v3 Announce Type: replace Abstract: Transforming neuroimaging data into clinically actionable biomarkers is a knowledge-intensive and labor-intensive process.
By Keqi Han, Songlin Zhao, Yao Su, Xiang Li, Yixuan Yuan, Lifang He, Carl Yang
arXiv:2609.37496v1 Announce Type: new
Abstract: Paired synthetic aperture radar (SAR) and electro-optical (EO) imagery is increasingly available across sensors, resolutions, and geographic regions. Y...
By Jeonghyeok Do, Munchurl Kim
arXiv:2609.00968v1 Announce Type: new
Abstract: SAR-to-EO image translation aims to generate electro-optical (EO) imagery from synthetic aperture radar (SAR) observations. Existing latent diffusion a...
By Jeonghyeok Do, Seungchul Lee, Munchurl Kim
The paper presents a lightweight method to adapt general‑purpose vision‑language models (VLMs) for multispectral and synthetic aperture radar (SAR) image understanding. By rendering each observation as five optical views and one SAR view, naming them in the prompt, and applying LoRA to the language network and selected visual transformer blocks, the authors enable VLMs to process band composites, spectral indices, and radar backscatter without retraining a new foundation model. On a balanced six‑class land‑cover benchmark from BigEarthNet‑v2, the adapted Qwen3‑VL achieves a micro F1 of 0.8275, and the same protocol improves four other VLMs and transfers to flood verification and captioning tasks.
"whyItMatters":"The study shows that existing VLMs can be repurposed for multispectral and SAR tasks through simple input rendering and compact LoRA adaptation, avoiding the need for dedicated encoders and domain pretraining."
By Shanji Liu, Kelu Yao, Junxiao Xue, Chenghui Lv, Xiangyang Miao, Yekai Huang, Yaying Chen, Chao Li
The paper demonstrates a lightweight method to adapt general-purpose vision‑language models (VLMs) for multispectral and synthetic aperture radar (SAR) image understanding. By rendering each observation as five optical views and one SAR view, naming them in the prompt, and applying LoRA to the language network and selected visual transformer blocks, the authors enable VLMs to process band composites, spectral indices, and radar backscatter without retraining a new foundation model. On a balanced six‑class land‑cover benchmark from BigEarthNet‑v2, the adapted Qwen3‑VL achieves a micro F1 score of 0.8275, and the same protocol improves four other VLMs and transfers to flood verification and captioning tasks.